Gastos Casa
29 mayo, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 20/3/2023 | Comida | 52314 | Tami | NA |
| 26/3/2023 | Comida | 24970 | Andrés | caramagnola |
| 28/3/2023 | Comida | 71805 | Tami | NA |
| 29/3/2023 | Electricidad | 42447 | Andrés | PAC ENEL 01686518 |
| 30/3/2023 | Netflix | 8320 | Tami | NA |
| 31/3/2023 | Comida | 13226 | Tami | NA |
| 31/3/2023 | Comida | 100000 | Andrés | wild foods |
| 31/3/2023 | Enceres | 15400 | Tami | Incoludido |
| 9/4/2023 | Gas | 67300 | Andrés | el de la derecha |
| 10/4/2023 | Comida | 61792 | Tami | NA |
| 17/4/2023 | Comida | 41602 | Tami | NA |
| 19/4/2023 | VTR | 21990 | Andrés | NA |
| 19/4/2023 | nacho | 55000 | Andrés | NA |
| 22/4/2023 | Comida | 19420 | Tami | NA |
| 23/4/2023 | Comida | 50617 | Tami | NA |
| 23/4/2023 | Crunchyroll | 49900 | Tami | NA |
| 23/4/2023 | Netflix | 5940 | Tami | NA |
| 28/4/2023 | Electricidad | 43471 | Andrés | NA |
| 29/4/2023 | Comida | 17000 | Andrés | pizza y dulces y nueces y almendras |
| 30/4/2023 | Comida | 84066 | Tami | NA |
| 30/4/2023 | Parafina | 38640 | Tami | NA |
| 7/5/2023 | Comida | 53654 | Tami | NA |
| 11/5/2023 | Cruz Verde | 21650 | Tami | Diolasa + propoleos |
| 14/5/2023 | Comida | 69636 | Tami | NA |
| 21/5/2023 | Comida | 71007 | Tami | NA |
| 25/5/2023 | Parafina | 19788 | Tami | NA |
| 27/5/2023 | Electricidad | 49600 | Andrés | NA |
| 27/5/2023 | Comida | 76142 | Tami | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 6.4272e+08 2 6.6405 0.0014 **
## lag_depvar 8.4253e+10 1 1740.9785 <2e-16 ***
## Residuals 2.8020e+10 579
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 1016.35 13441.33 0.017685
## 2-0 28360.421 22686.67 34034.17 0.000000
## 2-1 21131.583 17753.78 24509.39 0.000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
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## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
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## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 427 50594.68 15385.061
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1985.27418 4025.96889 -523.73985 2450.49615 -2941.29101 529.04712
## 8 9 10 11 12 13
## -5640.70271 -1204.79784 -3984.89472 -454.60045 -4971.10941 -1662.79242
## 14 15 16 17 18 19
## -952.77455 328.95374 -3279.94502 -426.25231 -2171.46447 6558.57806
## 20 21 22 23 24 25
## -1527.26481 -1212.54638 1467.84966 -1181.67493 235.03950 1699.79562
## 26 27 28 29 30 31
## -7084.81474 924.07105 8180.64184 459.30512 27.58937 -2361.12556
## 32 33 34 35 36 37
## 1599.77335 4605.56698 1185.89869 2452.83849 -1797.06379 4662.16715
## 38 39 40 41 42 43
## 4335.74786 -2221.87459 -2947.47067 -1097.69595 -10736.82031 7230.55793
## 44 45 46 47 48 49
## 2548.93154 1374.84728 8120.44789 747.39355 6586.58637 6803.56514
## 50 51 52 53 54 55
## -5764.67871 -4726.88467 -5027.70008 -7930.01189 6082.25934 -4081.90907
## 56 57 58 59 60 61
## -4922.64333 3802.56467 864.18441 -47.32971 128.98308 -5006.92536
## 62 63 64 65 66 67
## 18088.51830 3713.93501 -3560.40566 5979.16398 7426.20257 14754.19808
## 68 69 70 71 72 73
## 1880.37127 -13038.78722 -1231.26360 4701.67927 -4821.82702 -4364.33195
## 74 75 76 77 78 79
## -10487.73138 2414.37377 -5430.67678 1005.61737 -6910.42272 468.61966
## 80 81 82 83 84 85
## -2419.38030 -2763.83928 -4008.34051 -626.87764 2233.79021 3705.89630
## 86 87 88 89 90 91
## 449.50188 -505.48064 175.71972 4285.06333 -1152.41238 1153.57501
## 92 93 94 95 96 97
## -2055.11708 -1047.90457 168.60064 268.23444 -7487.89464 2345.10274
## 98 99 100 101 102 103
## -8628.98017 -3013.35569 -4119.64508 -1829.73004 -1352.14521 3094.79241
## 104 105 106 107 108 109
## -2398.44695 2531.48175 -1197.63696 929.76634 2557.48899 -3164.91816
## 110 111 112 113 114 115
## -4750.28740 -901.17618 1854.44545 11662.06302 -1201.81301 2697.23390
## 116 117 118 119 120 121
## 4303.76971 3563.58828 -1025.97881 -4657.73106 -3699.94745 2319.59432
## 122 123 124 125 126 127
## -1718.93611 1342.70290 8868.40196 907.87215 188.84136 -2469.12054
## 128 129 130 131 132 133
## 2686.32693 7095.64308 1091.50943 -8424.07280 1765.90742 4160.61395
## 134 135 136 137 138 139
## -3117.63790 -1397.34372 -842.34319 -3874.47390 1165.65608 -503.45413
## 140 141 142 143 144 145
## -2923.12483 1693.23089 -1892.55408 -7849.95075 1976.20927 -3522.82084
## 146 147 148 149 150 151
## 2044.60830 -295.35057 988.68052 -383.12432 1329.53369 1174.93718
## 152 153 154 155 156 157
## 3353.50364 -4844.70988 -1187.53648 -3253.77437 5922.49378 9751.62889
## 158 159 160 161 162 163
## -3378.48585 -4735.81813 3629.26160 253.48945 2764.20717 -5820.15805
## 164 165 166 167 168 169
## -6687.05442 4186.20352 17457.19998 3791.47315 -221.94801 -2279.89281
## 170 171 172 173 174 175
## -958.30245 3725.93710 -76.51138 -7930.27381 2960.49561 4439.32141
## 176 177 178 179 180 181
## 762.04312 8886.65507 -9068.90543 -3351.18200 -10644.45956 -11197.00891
## 182 183 184 185 186 187
## 1225.57496 9305.45106 -1356.15978 5998.63421 6660.34730 13295.66030
## 188 189 190 191 192 193
## 8629.02197 -3837.92816 2648.51088 10549.88506 -1424.52027 -2253.74452
## 194 195 196 197 198 199
## -10118.04123 -6264.49109 1297.64072 -5160.40820 -9747.84217 5387.86607
## 200 201 202 203 204 205
## -3024.95357 -1678.11131 -770.82102 6531.17948 9956.30623 699.91244
## 206 207 208 209 210 211
## 3042.36776 3224.01682 5918.66547 12988.29473 -5480.71487 -11134.42899
## 212 213 214 215 216 217
## -5570.41726 -10520.40809 -5056.87935 1530.23517 -12987.66348 16358.35082
## 218 219 220 221 222 223
## 7873.63753 1632.08204 26796.08971 12755.92322 7601.98049 14301.56094
## 224 225 226 227 228 229
## -3599.96557 -1478.87290 4005.75446 586.29138 2955.04267 9210.75819
## 230 231 232 233 234 235
## 6065.74774 -1659.65804 -1610.55819 9617.74699 -11284.31617 -7133.49775
## 236 237 238 239 240 241
## -8435.20853 -10039.75138 3093.60894 1392.79418 -8243.31812 -8971.03396
## 242 243 244 245 246 247
## 9079.55173 -7722.87257 2498.73146 -10267.51494 -4062.29335 1407.85399
## 248 249 250 251 252 253
## 1008.51732 -12295.02888 3614.28549 2065.77323 4235.23463 2185.39867
## 254 255 256 257 258 259
## -1096.38788 11199.50321 20993.87003 3396.86017 -4076.80302 4266.03572
## 260 261 262 263 264 265
## -1532.46210 3878.00481 -4704.60094 -10779.78100 -4670.57271 -483.62283
## 266 267 268 269 270 271
## -5148.40614 8798.25230 -4215.86042 4234.07981 -2042.13331 4485.24795
## 272 273 274 275 276 277
## 782.75484 7376.91676 -1310.17266 12113.31889 -4452.21234 1824.57397
## 278 279 280 281 282 283
## -273.97219 7940.40037 -4942.60724 -2646.61282 -11192.15990 -2646.56195
## 284 285 286 287 288 289
## 18672.58615 7880.96738 2854.94819 -508.03286 1013.14819 6499.94723
## 290 291 292 293 294 295
## 7000.70690 -18638.24387 -11086.71826 -8107.76107 9658.05675 3113.65930
## 296 297 298 299 300 301
## -1121.72839 27456.80189 10221.01457 5077.90070 9694.51018 3049.39281
## 302 303 304 305 306 307
## -849.69276 8056.86010 -24121.81067 -3461.05507 -112.63958 -6903.03156
## 308 309 310 311 312 313
## -3927.30830 2970.27323 -9135.76590 -3198.40879 -8154.23836 1581.76614
## 314 315 316 317 318 319
## -3115.74699 2083.68139 -4030.59792 27490.52603 -602.38956 3401.22721
## 320 321 322 323 324 325
## 10943.72455 5729.53526 32527.16344 5351.07038 -20704.95266 1934.38531
## 326 327 328 329 330 331
## 1248.43167 -6332.52251 -1629.91449 -33171.65821 909.39282 -2251.48796
## 332 333 334 335 336 337
## -32.49544 -3092.19393 4164.47900 -334.44140 -6843.46785 -3020.65304
## 338 339 340 341 342 343
## -2095.08419 -7579.51382 3938.94543 -1262.34482 -1625.66226 -879.89116
## 344 345 346 347 348 349
## 294.31784 605.49349 -1489.16636 -9318.98131 -13106.04606 2384.27373
## 350 351 352 353 354 355
## -4230.17952 -3567.73341 -5888.97066 1835.53836 1484.18707 2863.53867
## 356 357 358 359 360 361
## -3644.77282 -402.59624 793.14565 7134.02630 416.17702 99.95398
## 362 363 364 365 366 367
## 2719.28713 -2609.04704 -744.35592 -8611.86838 -4512.90880 -6103.96518
## 368 369 370 371 372 373
## -4848.13660 -7153.82528 5106.13588 482.63936 7237.24783 -7499.07620
## 374 375 376 377 378 379
## -2141.95094 -3267.06736 -2348.54127 -12338.57762 1999.32700 -10523.65457
## 380 381 382 383 384 385
## 5789.58563 9451.42348 3265.69362 -2253.63118 1742.00830 6883.56801
## 386 387 388 389 390 391
## 11561.57175 -5634.96214 -5222.02996 -36.00793 8681.42136 1947.92900
## 392 393 394 395 396 397
## 11351.45186 -9733.51363 2885.24597 824.75279 670.98940 -548.29678
## 398 399 400 401 402 403
## -462.53255 -14390.25985 8595.51557 -1079.84824 -1270.05101 7085.15032
## 404 405 406 407 408 409
## -7812.60565 -1191.04297 -2422.14418 -5708.02336 -2751.29617 -3805.86135
## 410 411 412 413 414 415
## -8642.88598 6238.02559 1765.22538 -7241.53307 -7570.53386 14334.75068
## 416 417 418 419 420 421
## 3958.93842 4636.69586 -7888.77920 -4614.73413 -2477.08862 2944.24686
## 422 423 424 425 426 427
## -13877.03364 -2679.62608 -8982.97849 3120.95085 7099.85993 6715.08617
## 428 429 430 431 432 433
## -3836.19311 -3977.87332 -4585.15494 -1657.83860 -5578.87905 -6499.74334
## 434 435 436 437 438 439
## -5829.04675 -1276.34031 -725.29507 -4847.23453 2706.21143 4974.90052
## 440 441 442 443 444 445
## -4909.14051 -2018.65805 1715.19606 -3692.16925 2975.50359 -6430.00548
## 446 447 448 449 450 451
## -11973.17982 -4393.15230 9763.30091 -1886.22526 4899.64679 -5711.45126
## 452 453 454 455 456 457
## -974.94751 532.75232 3179.08275 -12107.42559 3505.59120 -6552.40851
## 458 459 460 461 462 463
## 6660.35050 3168.95226 2671.21024 -3676.75255 2252.70730 157.46282
## 464 465 466 467 468 469
## 1957.20568 -353.91237 3515.30450 -2468.38513 5968.98836 -6766.21192
## 470 471 472 473 474 475
## -2803.81962 -2048.44497 -4507.97362 3146.30245 7957.95579 -5838.13024
## 476 477 478 479 480 481
## 1649.48777 -6008.67833 -2686.22661 2167.65323 -12765.92598 -9617.87057
## 482 483 484 485 486 487
## -1080.18199 149.46741 -824.42001 -1199.24457 -9438.99086 11224.53028
## 488 489 490 491 492 493
## 6398.19162 7599.44391 -5241.88482 5546.01723 9482.56585 6260.50796
## 494 495 496 497 498 499
## -13260.48421 -10390.24393 -3288.75772 -956.23776 -371.67888 -7469.39908
## 500 501 502 503 504 505
## 755.25235 4440.65327 5676.99826 843.78751 265.18101 -7054.68142
## 506 507 508 509 510 511
## 737.50916 -4876.41239 1995.11107 -1125.60809 -7987.55970 -448.27579
## 512 513 514 515 516 517
## -2516.48144 -431.13887 1492.52255 -9327.35540 -7616.97592 24422.46734
## 518 519 520 521 522 523
## 10026.40827 6099.27728 -5110.09428 3002.30828 17224.02559 11711.31963
## 524 525 526 527 528 529
## -23897.88759 -4879.56956 -3566.41211 4731.30095 -185.06373 -10932.24102
## 530 531 532 533 534 535
## 4536.52702 14069.92133 -4782.74748 4551.21415 5740.25725 -1596.37298
## 536 537 538 539 540 541
## -4356.37312 -6902.92539 -1945.37250 8476.49813 304.00054 -7965.14586
## 542 543 544 545 546 547
## 1973.19993 -434.75307 531.76618 -10862.52111 -10917.12360 2165.76505
## 548 549 550 551 552 553
## 7143.20898 -1154.31234 1000.28992 -7552.19443 8717.82566 1084.62506
## 554 555 556 557 558 559
## -11763.92196 9314.31463 8835.04896 295.16860 5044.76701 -3375.67334
## 560 561 562 563 564 565
## 14293.36542 21712.23869 -6213.98778 -9460.05133 6962.76629 424.47418
## 566 567 568 569 570 571
## 3646.59612 -7182.93161 -17135.33645 6797.16993 6593.05745 2079.46129
## 572 573 574 575 576 577
## 3280.20609 1958.58184 -1973.92040 14900.95823 -9432.85588 -6059.50105
## 578 579 580 581 582 583
## 8879.60982 3048.09604 -6352.04489 7683.73134 -3607.24940 -2594.79793
## 584
## 15876.42354
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17284.01 20113.03 24339.88 24059.65 26398.01 23747.67 24459.42 19721.94
## 10 11 12 13 14 15 16 17
## 19460.18 16819.89 17592.40 14342.65 14393.49 15053.90 16739.66 15070.40
## 18 19 20 21 22 23 24 25
## 16098.46 15475.99 22513.26 21603.12 21086.29 22964.25 22294.53 22942.92
## 26 27 28 29 30 31 32 33
## 24777.10 18744.21 20459.36 28246.69 28303.98 27978.98 25623.51 27017.00
## 34 35 36 37 38 39 40 41
## 30835.53 31181.73 32581.92 30108.40 34107.25 37294.87 34369.76 31200.98
## 42 43 44 45 46 47 48 49
## 30056.11 20695.73 28166.50 30587.44 31669.69 38464.18 37961.99 42594.43
## 50 51 52 53 54 55 56 57
## 46803.68 39548.17 34151.27 29205.73 22393.88 28643.77 25246.21 21567.44
## 58 59 60 61 62 63 64 65
## 25947.67 27199.19 27494.30 27903.50 23800.77 40286.21 42118.41 37394.69
## 66 67 68 69 70 71 72 73
## 41574.80 46459.09 57059.20 55085.64 40422.98 37944.75 40943.40 35279.90
## 74 75 76 77 78 79 80 81
## 30761.16 21523.91 24704.96 20656.67 22729.42 17657.52 19660.09 18891.55
## 82 83 84 85 86 87 88 89
## 17925.48 16006.73 17276.35 20861.39 25250.93 26234.48 26259.28 26872.08
## 90 91 92 93 94 95 96 97
## 30970.84 29808.85 30801.83 28878.62 28083.54 28449.34 28853.32 22471.75
## 98 99 100 101 102 103 104 105
## 25467.55 18542.50 17405.93 15459.16 15757.00 16430.06 20874.16 19963.52
## 106 107 108 109 110 111 112 113
## 23452.21 23243.52 24908.94 27767.35 25281.43 21747.60 22021.27 24650.65
## 114 115 116 117 118 119 120 121
## 35445.81 33650.19 35475.94 38455.13 40398.55 38101.73 32955.80 29320.55
## 122 123 124 125 126 127 128 129
## 31390.08 29681.01 30855.03 38406.27 38051.02 37118.55 34002.10 35771.93
## 130 131 132 133 134 135 136 137
## 41135.35 40579.22 31837.09 33093.81 36263.21 32696.77 31094.34 30185.19
## 138 139 140 141 142 143 144 145
## 26764.20 28169.60 27940.70 25641.77 27653.27 26286.81 19929.79 22940.96
## 146 147 148 149 150 151 152 153
## 20781.53 23739.64 24276.18 25856.41 26037.32 27680.92 28973.35 31986.14
## 154 155 156 157 158 159 160 161
## 27485.25 26752.92 24323.79 30180.23 41398.91 39739.82 37121.60 42109.80
## 162 163 164 165 166 167 168 169
## 43509.36 46903.44 42398.34 37735.51 43126.09 59324.10 61522.09 59946.32
## 170 171 172 173 174 175 176 177
## 56792.30 55201.78 57887.08 56917.42 49258.79 52064.25 55782.96 55818.92
## 178 179 180 181 182 183 184 185
## 62902.19 53465.18 50236.89 41104.29 32697.71 36183.55 46222.45 45681.94
## 186 187 188 189 190 191 192 193
## 51596.65 57304.91 68018.98 73268.07 67003.06 67195.26 74220.38 69924.46
## 194 195 196 197 198 199 200 201
## 65475.90 54788.49 48856.79 50271.98 45894.84 38113.71 44497.38 42736.11
## 202 203 204 205 206 207 208 209
## 42376.39 42851.68 49602.27 58434.66 58066.63 59780.41 61425.62 65192.56
## 210 211 212 213 214 215 216 217
## 74598.57 66732.00 54996.56 49639.84 40693.74 37670.91 40764.66 30848.65
## 218 219 220 221 222 223 224 225
## 47713.65 54987.63 55883.77 78503.65 85950.73 87941.15 95483.97 86492.73
## 226 227 228 229 230 231 232 233
## 80529.53 80114.14 76785.53 75952.38 80659.11 82014.66 76485.70 71729.25
## 234 235 236 237 238 239 240 241
## 77346.74 64079.93 56167.35 48169.47 39834.68 43999.78 46138.75 39631.32
## 242 243 244 245 246 247 248 249
## 33351.31 43568.02 37851.70 41762.23 34075.58 32789.72 36421.63 39227.46
## 250 251 252 253 254 255 256 257
## 30115.57 36015.66 39792.77 44954.32 47655.25 47151.07 57386.13 74771.43
## 258 259 260 261 262 263 264 265
## 74587.66 67941.11 69413.46 65658.42 67095.32 60892.92 50236.14 46288.91
## 266 267 268 269 270 271 272 273
## 46496.98 42628.60 51376.43 47673.35 51793.56 49922.18 53963.53 54257.65
## 274 275 276 277 278 279 280 281
## 60236.60 57885.97 67497.07 61460.71 61669.40 60029.03 65735.18 59505.76
## 282 283 284 285 286 287 288 289
## 56091.59 45710.70 44117.70 61239.75 66734.48 67141.32 64575.42 63668.62
## 290 291 292 293 294 295 296 297
## 67644.01 71529.24 52647.29 42812.62 36861.94 47117.34 50338.44 49458.06
## 298 299 300 301 302 303 304 305
## 73499.70 79407.10 80070.49 84653.46 82863.55 77925.57 81370.24 56429.48
## 306 307 308 309 310 311 312 313
## 52714.50 52396.32 46226.17 43453.44 47033.77 39633.55 38363.81 32960.09
## 314 315 316 317 318 319 320 321
## 36720.46 35907.03 39714.03 37711.33 63332.96 61187.92 62801.13 70748.18
## 322 323 324 325 326 327 328 329
## 73120.27 98439.22 96827.24 72811.76 71617.28 69985.09 61988.20 59128.80
## 330 331 332 333 334 335 336 337
## 29269.04 32933.06 33369.78 35674.91 35019.95 40750.16 41818.90 37096.80
## 338 339 340 341 342 343 344 345
## 36316.23 36442.09 31790.91 37751.63 38410.81 38667.61 39537.83 41312.36
## 346 347 348 349 350 351 352 353
## 43122.74 42875.98 35865.62 26493.58 31804.18 30672.45 30265.11 27896.75
## 354 355 356 357 358 359 360 361
## 32545.81 36276.18 40711.34 38911.88 40164.14 42288.97 49637.11 50184.19
## 362 363 364 365 366 367 368 369
## 50384.57 52832.05 50331.50 49779.58 42471.62 39686.25 35887.57 33680.40
## 370 371 372 373 374 375 376 377
## 29763.29 37004.79 39277.18 47112.50 41122.52 40573.21 39119.83 38655.58
## 378 379 380 381 382 383 384 385
## 29581.39 34150.23 27246.13 35413.15 45680.45 49223.20 47507.56 49486.57
## 386 387 388 389 390 391 392 393
## 55667.14 65092.25 58346.74 52850.15 52580.58 59913.21 60433.26 69046.80
## 394 395 396 397 398 399 400 401
## 58221.75 59778.68 59341.58 58828.73 57325.25 56094.69 42937.48 51468.56
## 402 403 404 405 406 407 408 409
## 50475.34 49448.14 55808.75 48398.61 47714.14 46051.45 41756.15 40594.29
## 410 411 412 413 414 415 416 417
## 38670.46 32802.12 40624.92 43532.68 38238.82 33358.25 48135.49 51955.88
## 418 419 420 421 422 423 424 425
## 55860.21 48377.16 44723.80 43408.18 46971.89 35464.48 35195.41 29490.62
## 426 427 428 429 430 431 432 433
## 35045.00 43319.77 50168.19 46954.16 44041.44 40986.12 40875.02 37375.17
## 434 435 436 437 438 439 440 441
## 33538.05 30789.63 32355.72 34193.38 32210.65 37045.96 43212.14 39985.09
## 442 443 444 445 446 447 448 449
## 39692.95 42680.31 40579.78 44544.01 39821.04 30910.15 29754.98 41039.94
## 450 451 452 453 454 455 456 457
## 40723.50 46338.88 42002.66 42350.10 43960.35 47655.00 37593.41 42411.98
## 458 459 460 461 462 463 464 465
## 37864.22 45385.33 48883.08 51487.04 48237.29 50563.25 50763.51 52499.48
## 466 467 468 469 470 471 472 473
## 52000.27 54925.39 52270.58 57289.78 50592.39 48218.44 46813.55 43459.27
## 474 475 476 477 478 479 480 481
## 47191.62 54607.70 49069.94 50762.39 45584.23 43973.49 46788.50 36269.73
## 482 483 484 485 486 487 488 489
## 29872.04 31729.53 34409.13 35889.67 36849.42 30530.47 42981.38 49599.41
## 490 491 492 493 494 495 496 497
## 56386.46 51131.41 55933.86 63519.21 67306.48 53649.82 44287.33 42324.81
## 498 499 500 501 502 503 504 505
## 42645.96 43432.11 37953.75 40337.49 45605.43 51251.07 51956.25 52066.11
## 506 507 508 509 510 511 512 513
## 45807.92 47139.41 43422.32 46160.32 45828.13 39583.70 40707.62 39888.00
## 514 515 516 517 518 519 520 521
## 40986.62 43609.93 36495.40 31804.68 55543.02 63652.01 67281.81 60702.83
## 522 523 524 525 526 527 528 529
## 62033.83 75533.39 82465.89 57574.86 52477.41 49192.70 53543.92 53053.38
## 530 531 532 533 534 535 536 537
## 43299.19 48259.36 60839.60 55395.21 58771.31 62733.80 59805.09 54867.35
## 538 539 540 541 542 543 544 545
## 48371.09 47035.50 54922.29 54674.29 47281.51 49491.04 49318.81 50008.24
## 546 547 548 549 550 551 552 553
## 40716.55 32604.09 36918.36 44983.46 44781.71 46476.77 40524.60 49480.37
## 554 555 556 557 558 559 560 561
## 50628.35 40472.40 49952.81 57765.69 57134.66 60709.53 56503.63 68189.48
## 562 563 564 565 566 567 568 569
## 84772.13 74926.05 63562.23 67953.38 66089.69 67268.79 58892.34 42983.12
## 570 571 572 573 574 575 576 577
## 49947.23 55814.82 56990.08 59052.42 59695.35 56840.04 69008.86 58449.79
## 578 579 580 581 582 583 584
## 52212.68 59765.90 61260.33 54398.27 60624.96 56229.23 53292.58
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8373
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 6.640495 0.5449058 3.360267
## t2* 1740.978542 23.6431965 221.123654
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.422221 6.779964 13.4006
## 2 lag_depvar 1426.500391 1750.714549 2151.7093Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon May 29 00:54:16 2023
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## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 0.0000 | 5.410333 | 5.629750 | 6.359175 |
| Comida | 370.2575 | 310.278417 | 314.087500 | 343.358350 |
| Comunicaciones | 0.0000 | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | 31.5895 | 47.072333 | 38.297667 | 32.318925 |
| Enceres | 33.2250 | 20.086417 | 17.443792 | 25.492375 |
| Farmacia | 4.9950 | 1.831667 | 7.913875 | 9.458850 |
| Gas/Bencina | 44.0600 | 44.325000 | 28.954333 | 26.956100 |
| Diosi | 12.1450 | 31.180667 | 41.934250 | 37.511450 |
| donaciones/regalos | 0.0000 | 0.000000 | 7.170083 | 6.867975 |
| Electrodomésticos/ Mantención casa | 0.0000 | 3.944000 | 30.269500 | 20.736700 |
| VTR | 10.9950 | 25.156667 | 22.121792 | 20.106700 |
| Netflix | 5.6450 | 7.151583 | 7.090167 | 7.292775 |
| Otros | 0.0000 | 3.151083 | 1.575542 | 0.945325 |
| Total | 512.9120 | 499.588167 | 522.488250 | 537.404700 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1986, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-06-09 00:04:58 sería de: 36.396 pesos// Percentil 95% más alto proyectado: 39.596,03
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36066.76 | 36065.69 |
| Lo.80 | 36071.90 | 36069.74 |
| Point.Forecast | 36395.64 | 37104.26 |
| Hi.80 | 38191.54 | 41922.45 |
| Hi.95 | 39177.83 | 44473.05 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3254 1006.1984
## s.e. 0.1382 33.3749
##
## sigma^2 = 27182: log likelihood = -331.76
## AIC=669.53 AICc=670.04 BIC=675.32paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2965 656.0032 11.4970
## s.e. 0.1413 367.8739 12.0162
##
## sigma^2 = 27292: log likelihood = -331.33
## AIC=670.66 AICc=671.53 BIC=678.39forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 767.4879 | 664.4556 | 699.4717 |
| Lo.80 | 884.8408 | 782.7447 | 812.0492 |
| Point.Forecast | 1106.5258 | 1006.1981 | 1024.7130 |
| Hi.80 | 1328.2108 | 1229.6515 | 1285.1505 |
| Hi.95 | 1445.5638 | 1347.9405 | 1423.0177 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.28 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.1 xts_0.13.1
## [13] forecast_8.21 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.2 forcats_1.0.0
## [22] dplyr_1.1.2 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
## [28] sjPlot_2.8.14 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.1 httr_1.4.6
## [34] readxl_1.4.2 zoo_1.8-12 stringr_1.5.0
## [37] stringi_1.7.12 data.table_1.14.8 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.8 readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.5
## [10] fansi_1.0.4 ggfortify_0.4.16 magrittr_2.0.3
## [13] tzdb_0.4.0 modelr_0.1.11 vroom_1.6.3
## [16] timechange_0.2.0 anytime_0.3.9 tseries_0.10-54
## [19] colorspace_2.1-0 xfun_0.39 crayon_1.5.2
## [22] jsonlite_1.8.4 lme4_1.1-33 glue_1.6.2
## [25] r2d3_0.2.6 gtable_0.3.3 emmeans_1.8.6
## [28] sjstats_0.18.2 sjmisc_2.8.9 car_3.1-2
## [31] quantmod_0.4.22 abind_1.4-5 mvtnorm_1.1-3
## [34] DBI_1.1.3 ggeffects_1.2.2 Rcpp_1.0.10
## [37] viridisLite_0.4.2 xtable_1.8-4 performance_0.10.3
## [40] bit_4.0.5 htmlwidgets_1.6.2 timeSeries_4030.106
## [43] gplots_3.1.3 ellipsis_0.3.2 spatial_7.3-14
## [46] pkgconfig_2.0.3 farver_2.1.1 nnet_7.3-16
## [49] sass_0.4.5 dbplyr_2.3.2 janitor_2.2.0
## [52] utf8_1.2.3 tidyselect_1.2.0 labeling_0.4.2
## [55] rlang_1.1.0 munsell_0.5.0 cellranger_1.1.0
## [58] tools_4.1.2 cachem_1.0.7 cli_3.6.1
## [61] generics_0.1.3 sjlabelled_1.2.0 broom_1.0.4
## [64] evaluate_0.20 fastmap_1.1.1 yaml_2.3.7
## [67] knitr_1.43 bit64_4.0.5 caTools_1.18.2
## [70] forge_0.2.0 nlme_3.1-153 slam_0.1-50
## [73] xml2_1.3.3 tokenizers_0.3.0 compiler_4.1.2
## [76] rstudioapi_0.14 curl_5.0.0 bslib_0.4.2
## [79] highr_0.10 fBasics_4022.94 Matrix_1.5-4.1
## [82] its.analysis_1.6.0 nloptr_2.0.3 urca_1.3-3
## [85] vctrs_0.6.1 pillar_1.9.0 lifecycle_1.0.3
## [88] lmtest_0.9-40 jquerylib_0.1.4 estimability_1.4.1
## [91] bitops_1.0-7 insight_0.19.2 R6_2.5.1
## [94] KernSmooth_2.23-20 janeaustenr_1.0.0 codetools_0.2-18
## [97] gtools_3.9.4 boot_1.3-28 MASS_7.3-54
## [100] assertthat_0.2.1 rprojroot_2.0.3 withr_2.5.0
## [103] fracdiff_1.5-2 bayestestR_0.13.1 parallel_4.1.2
## [106] hms_1.1.3 quadprog_1.5-8 timeDate_4022.108
## [109] minqa_1.2.5 snakecase_0.11.0 rmarkdown_2.21
## [112] carData_3.0-5 TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))